Analysis of NDVI variations and its climate factors based on geographically and temporally weighted regression: A case study in Inner Mongolia, China

Abstract

Exploring the spatiotemporal trajectory of vegetation growth in response to climate factors is of paramount importance for maintaining global ecosystem stability and sustainable regional development. In this paper, based on remote sensing and geographic information system (GIS) technology, the geographically and temporally weighted regression (GTWR) model was applied to investigate the effect of climate change on normalized difference vegetation index (NDVI) index to capture spatial and temporal heterogeneity. As a case study, the GTWR model was compared with global ordinary least squares (OLS), temporally weighted regression (TWR), and geographically weighted regression (GWR) in terms of modeling NDVI in Inner Mongolia, China from 2014 to 2018. Indicated by the goodness of fit of the model, the results confirmed the effectiveness of the GTWR method and its predominance over OLS, TWR, and GWR models, highlighting the necessity of incorporating both spatial and temporal nonstationarity for modeling spatiotemporal variation in NDVI.